Genetic Algorithm Learning and Economic Evolution

Abstract

This paper tries to connect the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a specific form of evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria which during the learning process build up to finally reach a neighborhood of an evolutionarily stable state. In order to clarify this point, a concept of evolutionary stability of genetic populations will be developed. Thus, in a second part of the paper, it becomes possible to explain both, the reasons for the specific dynamics of standard GA learning models and the different kind of dynamics of GA learning models which use extensions to the standard GA.